The Genomic Footprint of Natural Selection: Inference from Population Data

Natural selection acts on heritable variation that influences survival and reproductive success within specific environmental contexts. With the explosion of genomic data from both living organisms and archaeological remains, investigating the molecular basis of natural selection has become an exceptionally dynamic area of research.

This course examines the concepts and analytical methods used to detect genomic signatures of natural selection, with a particular focus on adaptive (positive or Darwinian) selection in humans.

Through a combination of interactive lectures and hands-on sessions, participants will develop a solid theoretical background in population genetics and measures of genetic diversity relevant to studying selection at the genomic level. The course covers established and emerging bioinformatic approaches for identifying genomic footprints of natural selection, including analyses of modern genotype data in isolated populations, detection of adaptive introgression from other species (such as archaic humans into modern humans), inference of recent selection driven by admixture (for instance, in Latin American populations), and analyses of time-series from ancient DNA data to explore how shifts in genetic variant frequencies through time reveal natural selection in action.

Throughout the course, practical and conceptual challenges, such as demographic confounding, limited statistical power, and difficulties in biological interpretation, will be addressed. By the end of the course, participants will be able to critically assess genomic evidence for natural selection and to understand how such inferences inform research on human evolution and biomedicine.

Places are limited to 16 participants.